TECHNOLOGY & DATA MANAGEMENT O


ne of the biggest pain points in clinical trials is patient recruitment. Clinical trials often have strict eligibility


criteria that look for suitable subjects who are healthy enough to participate. However, nding t  ealty atients is time- consuming and costly.


Dr Daniel Vorobiof, chief medical director at patient-centric network platform Belong.Life, says clinical trials have been in a problematic situation for many years, with only a small percentage of patients being accrued into clinical trials. “Many patients have never heard about clinical trials and their own doctors have never talked to them about it,” he says. Data analysis by Clinical Trials Arena


revealed that the most common reason for trial termination is a low accrual rate. A different analysis noted that 86% of all trials do not meet enrolment timelines and almost one-third of Phase III trials fail because of slow enrolment. However, the prevalence of trial terminations


due to low accrual is decreasing, possibly due to the extended use of technology-aided soltions, inclding artificial intelligence AI. Experts shared their thoughts with Clinical Trials Arena on how AI-powered trial matching can accelerate patient identification. till, as with many technology solutions, AI is not perfect and certain limitations can slow down the process.


eneting all stakeolders People who stop responding to existing therapies always look for the next best thing, and sometimes the only option left is a clinical trial, says elong.ifes chief technology officer, Irad Deutsch. “We understood that in order to scale up, we cannot use a 100% human-based effort to provide the support and answers to all these demands,” Deutsch notes. Belong.Life offers patient support across


various therapy areas, but there is a high demand for clinical trials among oncology patients. Over the past seven years, Deutsch and his team have developed an AI-powered technology that automates most of the trial-matching process.


AI trial-matching platforms also increase awareness among physicians, says Dr Michel van arten, chief eective officer at myTomorrows, which recently released a physician-focused clinical trial search tool TrialSearch AI. The tool streamlines the matching process and gives physicians the opportunity to refer a patient to a clinical trial, he explains. MyTomorrows investigators conducted a test run on how the TrialSearch AI works by creating  fictitios patient profiles across  different diseases. The tool, which operates on a Large Language Model (LLM), was able to reduce pre-screening checking time for physicians by 90%.


Other use cases also demonstrated the efficiencies of AIbased clinical trial matching. A 102-patient Australian study demonstrated 95.7% accuracy for exclusion and 91.6% accuracy for overall eligibility assessment among cancer patients. nsrprisingly, this technology benefits both pharma companies and clinical trial sponsors. Van Harten explains that if trial enrolment is faster, sponsors can reach the finish line more quickly. “If a trial was earlier enrolled and completed, the faster they can get to commercialisation,” he adds.


Limitations in sight or AI tools to operate efficiently, the data input in public clinical trial registries must be up to a certain standard. However, because data and information are submitted by humans, data standardisation is a crucial process. Both TrialSearch AI and Belong.Life AI engines require standardised and structured databases to operate.


As with humans, AI also has language


barriers. Currently, the Belong.Life engine supports English, French, German, Spanish and Hebrew, while the TrialSearch AI platform uses data from ClinicalTrials.gov and the European Clinical Trials Register. Data privacy is another hot topic in the


world of AI. “You need to adhere to the highest standards when it comes to protecting their (patients’) data and privacy,” van Harten says. However, as with the wider application of AI, AI-powered trial-matching platforms are not


Outsourcing In Clinical Trials | 31


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